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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
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Eval Results
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  1. README.md +37 -32
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8364779874213837
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4547
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- - Accuracy: 0.8365
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  ## Model description
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@@ -67,35 +67,40 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6516 | 0.34 | 15 | 0.6155 | 0.7421 |
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- | 0.5163 | 0.67 | 30 | 0.5604 | 0.7421 |
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- | 0.5583 | 1.01 | 45 | 0.5582 | 0.7579 |
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- | 0.5323 | 1.34 | 60 | 0.5358 | 0.7044 |
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- | 0.5691 | 1.68 | 75 | 0.5361 | 0.7736 |
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- | 0.5143 | 2.01 | 90 | 0.5042 | 0.7421 |
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- | 0.4711 | 2.35 | 105 | 0.5435 | 0.7075 |
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- | 0.5742 | 2.68 | 120 | 0.4918 | 0.7673 |
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- | 0.5406 | 3.02 | 135 | 0.4630 | 0.7987 |
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- | 0.4454 | 3.35 | 150 | 0.5241 | 0.7453 |
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- | 0.348 | 3.69 | 165 | 0.4116 | 0.8113 |
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- | 0.3441 | 4.02 | 180 | 0.4560 | 0.7987 |
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- | 0.6001 | 4.36 | 195 | 0.4411 | 0.8113 |
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- | 0.2765 | 4.69 | 210 | 0.4282 | 0.8270 |
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- | 0.4746 | 5.03 | 225 | 0.4850 | 0.7642 |
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- | 0.2547 | 5.36 | 240 | 0.4294 | 0.8176 |
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- | 0.3734 | 5.7 | 255 | 0.4351 | 0.8270 |
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- | 0.2776 | 6.03 | 270 | 0.4395 | 0.8176 |
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- | 0.3024 | 6.37 | 285 | 0.4005 | 0.8491 |
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- | 0.2034 | 6.7 | 300 | 0.4476 | 0.8113 |
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- | 0.2668 | 7.04 | 315 | 0.4359 | 0.8113 |
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- | 0.267 | 7.37 | 330 | 0.4509 | 0.8019 |
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- | 0.1185 | 7.71 | 345 | 0.4554 | 0.8208 |
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- | 0.1785 | 8.04 | 360 | 0.4258 | 0.8208 |
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- | 0.1733 | 8.38 | 375 | 0.4197 | 0.8270 |
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- | 0.2107 | 8.72 | 390 | 0.6167 | 0.7484 |
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- | 0.1244 | 9.05 | 405 | 0.5048 | 0.7925 |
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- | 0.1648 | 9.39 | 420 | 0.4921 | 0.8050 |
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- | 0.2374 | 9.72 | 435 | 0.4547 | 0.8365 |
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8056426332288401
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5189
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+ - Accuracy: 0.8056
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6404 | 0.34 | 15 | 0.6380 | 0.7147 |
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+ | 0.597 | 0.67 | 30 | 0.5823 | 0.7147 |
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+ | 0.4913 | 1.01 | 45 | 0.5644 | 0.7335 |
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+ | 0.62 | 1.34 | 60 | 0.5351 | 0.7555 |
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+ | 0.442 | 1.68 | 75 | 0.5699 | 0.7335 |
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+ | 0.47 | 2.01 | 90 | 0.5137 | 0.7774 |
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+ | 0.416 | 2.35 | 105 | 0.4972 | 0.7680 |
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+ | 0.5087 | 2.68 | 120 | 0.4796 | 0.7868 |
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+ | 0.4819 | 3.02 | 135 | 0.4808 | 0.7712 |
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+ | 0.3914 | 3.35 | 150 | 0.5446 | 0.7335 |
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+ | 0.4302 | 3.69 | 165 | 0.4864 | 0.7962 |
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+ | 0.3179 | 4.02 | 180 | 0.4928 | 0.7994 |
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+ | 0.3324 | 4.36 | 195 | 0.5233 | 0.7304 |
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+ | 0.3031 | 4.69 | 210 | 0.4795 | 0.7962 |
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+ | 0.3479 | 5.03 | 225 | 0.4982 | 0.7524 |
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+ | 0.4205 | 5.36 | 240 | 0.5121 | 0.7868 |
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+ | 0.2069 | 5.7 | 255 | 0.4925 | 0.7900 |
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+ | 0.3349 | 6.03 | 270 | 0.4900 | 0.7994 |
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+ | 0.3206 | 6.37 | 285 | 0.4830 | 0.7900 |
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+ | 0.2496 | 6.7 | 300 | 0.4920 | 0.8025 |
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+ | 0.1587 | 7.04 | 315 | 0.5077 | 0.7994 |
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+ | 0.3422 | 7.37 | 330 | 0.4959 | 0.7806 |
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+ | 0.2785 | 7.71 | 345 | 0.4861 | 0.7994 |
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+ | 0.2225 | 8.04 | 360 | 0.4819 | 0.8276 |
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+ | 0.1682 | 8.38 | 375 | 0.5316 | 0.7774 |
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+ | 0.2446 | 8.72 | 390 | 0.5200 | 0.7962 |
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+ | 0.2499 | 9.05 | 405 | 0.5491 | 0.7586 |
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+ | 0.2536 | 9.39 | 420 | 0.4917 | 0.8245 |
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+ | 0.2374 | 9.72 | 435 | 0.5022 | 0.8150 |
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+ | 0.161 | 10.06 | 450 | 0.5117 | 0.8150 |
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+ | 0.1604 | 10.39 | 465 | 0.5531 | 0.7555 |
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+ | 0.0895 | 10.73 | 480 | 0.5473 | 0.7837 |
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+ | 0.2949 | 11.06 | 495 | 0.5573 | 0.7868 |
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+ | 0.172 | 11.4 | 510 | 0.5189 | 0.8056 |
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  ### Framework versions
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